1. Field of the Invention
The present invention relates to a fuzzy control method for adjusting a semiconductor machine. In particular, the present invention relates to a fuzzy control method for adjusting a semiconductor machine by utilizing fuzzy control to adjust the control parameters of the semiconductor machine in the semiconductor manufacturing process.
2. Description of Related Art
The semiconductor manufacturing process is a core manufacturing process for the electronic, communication, optical-electrical and solar energy products, and is the basic technology for the wafer foundry. The manufacturing process integration and the quality of the semiconductor manufacturing process has the following trends, including (1) using the control technology to enhance the manufacturing process performance, (2) using the statistics method to monitoring the semiconductor machine performance, and (3) using the semiconductor machine performance to verify the manufacturing process performance. For example, in order to monitor semiconductor machine and reduce the cost and the risk, the semiconductor manufacturer develops the Metrology integration system technology and the automatic real-time monitoring system, such as the advanced process control (APC). The APC can be divided onto the manual experience control, the run-to-run (R2R or RbR), the model-based process control (MBPC), and the fault detection and classification (FDC). The R2R is designed to integrate the semiconductor machine, the manufacturing process operation, the status variations, and the chip measurement quality variations on-line and in real-time, and use the manufacturing process model estimation to feed back and adjust the manufacturing process parameters on-line. The FDC estimates the semiconductor machine failure and the element failure in real time and uses the fault classification technology the find out the failure or the abnormal reason in order to monitor the equipment health condition and achieve the predictive maintenance mechanism. The control chip and the maintenance are thereby reduced.
Please refer to FIG.1, a conventional method for predicting the manufacturing process parameter variation, and a system, a storage medium thereof is described as follows. The method determines whether at least one manufacturing process parameter changes or not, and includes the following steps. At least one detection machine is used for generating at least one statistical manufacturing process control table (S10). The statistical manufacturing process control table is inputted, at least one detection parameter and at least one manufacturing process parameter corresponding to the statistical manufacturing process control table are defined, and a relationship table between the detection parameters and the manufacturing process parameters is defined (S20). Whether the statistical manufacturing process control table meets a pre-determined condition is checked (S30). When the pre-determined condition is met, a first parameter corresponding to the pre-determined condition is selected from the detection parameters (S40). According to the relationship table, a second parameter corresponding to the first parameter is selected from the manufacturing process parameters (S50). The relative manufacturing process parameter record corresponding to the second parameter is inputted (S60). According to the manufacturing process parameter record, a parameter variation is determined (S70). The parameter variation of the manufacturing process machine is adjusted (S80). Finally, the adjusted manufacturing process parameter is used for manufacturing a semiconductor product (S90).
The prior art focuses on a single manufacturing process machine and uses the yield rate to trace back the obvious and easy manufacturing process parameter variation or change to adjust the manufacturing process parameter of the machine. The accuracy of this method is easily affected by the pre-manufacturing process, or the characteristic of the manufacturing process machine. Therefore, the manufacturing process machine usually is over-controlled or the manufacturing process parameter is extremely adjusted. The reliability is reduced, and the manufacturing cost is expensive.